Rate SSIM Based Preprocessing for Video Coding

Image Denoising is a consistent problem from long period of time and still a challenging task for researchers. There evolved many techniques for image denoising which involves filtering techniques in spatial domain, Transform techniques in transform domain (Sekhar et al. in IRECOS 10(10):1012–1017, 2015 [1]), and more recently evolutionary computing tools (ECT) and genetic algorithms proved more effective in denoising of images. There are many ECT available which can be applied for denoising problem (Sekhar et al. in JGIM 25(4) 2017, [2]). In this paper we made an attempt to Denoise both color and grayscale images by applying a new ECT which emerged out with more efficient results. Peak Signal to noise ratio (PSNR), Structural Similarity Index Metric (SSIM), Mean Structural Similarity Index Metric (MSSIM), etc., are considered in this paper as Image quality Assessment metrics. Comparison of proposed method is also compared with state-of-the-art techniques.

Scalable video coding (SVC) has aroused a wide interest in the areas of video coding and transmission technology, since it provides desirable features for heterogeneous error-prone network environments. The layered video structure allows not only adaptation to the available bandwidth but also a device adaptation capability via multiple decodable sub-streams. In this paper, we focus on SVC Medium- Grain Scalability (MGS) and investigate the end user's quality of experience (QoE) in error-prone transmission conditions. We describe our error detection and concealment implementation on the JSVM 9.15 reference decoder and assess its effectiveness through several quality indicators. Results show an improvement of the proposed concealment strategy when compared to the standard Frame Copy (FC) and Interpolation, in terms of Peak Signal-to-Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) quality metrics. This improvement is evident in the case of concealment on quality enhancement layers, since video fluidity is preserved assuring an acceptable QoE to the end users.

Noise Removal in medical images is a requisite pre-processing step for medical image analysis. In this paper, we present three deep learning architectures: Convolutional Auto-encoders, U-Net and a Double U-Net architecture utilizing convolution layers for medical Image De-noising. We use PSNR(Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric) to compare the results of the networks.In our experiments, the variant of the U-Net architecture implemented performed better than the two architectures in terms of its SSIM.

Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99m Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time images from short-acquisition-time pediatric 99m Tc-DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. One hundred and fifty-five cases that underwent pediatric 99m Tc-DMSA planar imaging as dynamic data for 10min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full-time images from short-time images. We used the normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full-time images. In addition, the renal uptake of 99m Tc-DMSA was calculated, and the difference in renal uptake from the reference full-time images was assessed using scatter plots with Pearson correlation and Bland-Altman plots. The predicted full-time images from the deep learning models showed a significant improvement in image quality compared to the short-time images with respect to the reference full-time images. In particular, the predicted full-time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4-0.5] %) and the highest PSNR (55.4 [54.7-56.1] dB) and SSIM (0.997 [0.995-0.997]). For renal uptake, an extremely high correlation was achieved in all short-time and three predicted full-time images (R2 >0.999 for all). The Bland-Altman plots showed the lowest bias (-0.10) of renal uptake in ResUnet, while short-time images showed the lowest variance (95% confidence interval: -0.14, 0.45) of renal uptake. Our proposed method is capable of producing images that are comparable to the original full-acquisition-time images, allowing for a reduction of acquisition time/injected dose in pediatric 99m Tc-DMSA planar imaging.

Adaptive quantization (AQ) proves to be an effective tool to improve coding performance. In this paper, we propose an adaptively spatiotemporal perception aware quantization algorithm to increase subjective coding performance. First, the perceptual complexity models are conducted with spatial and temporal characteristics to measure the spatiotemporally perceptual redundancies, respectively. With the help of the models, the adaptively spatial and temporal quantization parameter (QP) offsets are then calculated for each coding tree unit (CTU), respectively. Finally, the perceptually optimal Lagrange multiplier of each CTU is determined with the spatial-temporal QP offset. Experimental results show that the proposed algorithm reduces 8.6% BD-Rate with SSIM (Structural Similarity Index Metric) in average over the AVS2 (the second generation of Audio Video Coding Standard) reference software RD17.0 in Low-Delay P (LDP) configurations. The subjective assessment proves the proposed algorithm can significantly reduce the bit rates with the same subjective quality.

Joint Model (JM) reference software is used for academic reference of H.264 and it was developed by JVT (Joint Video Team) of ISO/IEC MPEG & ITU-T VCEG (Video coding experts group). The Intel IPP (Integrated Performance Primitives) H.264 is a product of Intel which uses IPP libraries and SIMD instructions available on modern processors. The Intel IPP H.264 is multi-threaded and uses CPU optimized IPP routines. In this paper, JM 15.1 and Intel IPP H.264 codec are compared in terms of execution time and video quality of the output decoded sequence. The metrics used for comparison are SSIM (Structural Similarity Index Metric), PSNR (Peak-to-Peak Signal to Noise Ratio), MSE (Mean Square Error), motion estimation time, encoding time, decoding time and the compression ratio of the H.264 file size (encoded output). Intel IPP H.264 clearly emerges as the winner against JM 15.1 in all parameters except for the compression ratio of H.264 file size.

A fusion-domain color image watermarking based on Haar transform and image correction

Focusing on enhancing the security and privacy of electronic medical images (EMI) which have varying properties and larger in number before they are stored or transmitted by hospitals, labs, and researchers or while they reside in third party storage. EMI should be anywhere, anytime accessible for referencing and medical diagnosis. Therefore, should be accessible in real-time through real-time applications (RTA). This research work uses a large data set having 5856 X-ray images of a total size of 1.6 GB in JPEG format each of these X-ray images vary in properties such as size and dimensions. Two methods have been implemented for enhancing security and privacy Edge-Based Steganography (EBS) and Block-Based Steganography (BBS) in the region of no interest (RONI) of the normalized cover image. Both methods are compared for enhancement concerning steganography properties such as payload capacity, imperceptibility, security, reduction in computational time, and lesser computational complexity which increases their applicability for RTA. RTA makes EMI accessible on heterogeneous devices, therefore, increasing its scalability and usage. This paper performs comparative evaluation based on standardized tests such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), Coefficient of Variation (CV), and Pearson Correlation (R), etc. Results show that both methods achieved a 100% embedding rate, SSIM value close to 1, and low average MSE value of 0.005 for EBS secret images and 0.003 for BBS secret images. Results indicate both methods are capable of hiding large data sets within minutes therefore making them both suitable for RTA while the EBS method takes 2.3 minutes which is less than BBS’s 3.6 minutes encoding time for 5856 images. Therefore, the EBS method was found more suitable than the BBS method based on computational time.

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

Melanoma and non-melanoma skin cancers have shown a rapidly increasing incidence rate, pointing to skin cancer as a major problem for public health. When analyzing these lesions in dermoscopic images, the hairs and their shadows on the skin may occlude relevant information about the lesion at the time of diagnosis, reducing the ability of automated classification and diagnosis systems. In this work, we present a new approach for the task of hair removal on dermoscopic images based on deep learning techniques. Our proposed model relies on an encoder-decoder architecture, with convolutional neural networks, for the detection and posterior restoration of hair's pixels from the images. Moreover, we introduce a new combined loss function in the network's training phase that combines the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> distance, the total variation loss, and a loss function based on the structural similarity index metric. Currently, there are no datasets that contain the same images with and without hair, which is necessary to quantitatively evaluate our model. Thus, we simulate the presence of hair in hairless images extracted from publicly known datasets. We compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with simulated hair. Finally, the Wilcoxon signed-rank test is used to compare the methods. The results, both qualitatively and quantitatively, demonstrate the effectiveness of our model and how our loss function improves the restoration ability of the proposed model.

Each image metric represents different characteristics of images. For instance, similarity metrics, e.g. Structural Similarity Index Metric (SSIM) or Pearson Correlation Coefficient (PCC), utilize correlation between two images to calculate similarity between them; error metrics, e.g. Mean Absolute Error (MAE) or Root-Mean Squared Error (RMSE), compute the pixel-wise error between them according to different norms. As each of them highlights different aspects, a choice of the metric for an application may depend upon characteristics of the images. In this paper, we will show some tomographic reconstructions of dense-layered binary-phase objects, and as the objects are binary, we propose Probability of Error (PE) as an image metric for the assessment of the reconstructions in contrast to other metrics that are not constrained to the range of values. PE is equivalent to Bit-Error Rate (BER) in digital communications as both of the signals of interest are binary, and we are interested to a bit-wise deviation of the reconstructions of their corresponding ground truth images.

Style transfer is an optimizing technique that aims to blend style of input image to content image. Deep neural networks have previously surpassed humans in tasks such as object identification and detection. Deep neural networks, on the contrary, had been lagging behind in generating higher quality creative products until lately. This article introduces deep-learning techniques, which are vital in accomplishing human characteristics and open up a new world of prospects. The system employs a pre-trained CNN so that the styles of the provided image is transferred to the content image to generate high quality stylized image. The designed systems effectiveness is evaluated based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM), it is noticed that the designed method effectively maintains the structural and textural information of the cover image.

In this study, an analog circuit design instead of using a software is utilized to remove Salt and Pepper (S&P) noise in image. For this purpose, a digital image filtering method which is called median filter is carried out. In median filter method, a current-mode nine-input one-output circuit architecture is preferred since 3 × 3 filtering mask is employed. Median filter circuit is based on a modified bitonic sort network and consists of 26 max/min selector circuits. A current-mode two-input two-output circuit is designed as a max/min selector and there is no biasing current source in the max/min selector structure. In analysis part, two 150 × 150 images with different amount of S&P noise are used and the noise in image is successfully reduced with the help of the proposed circuit design. The performance of the suggested median filter is also verified through the quality measurement parameters such as Structural Similarity Index Metric, Mean Squared Error, Mean Absolute Error and Peak Signal to Noise Ratio. TSMC CMOS 0.18 µm process model is utilized to simulate the introduced median filter circuit.

The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30–120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30–120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.

Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges.